Multilevel IRT Modeling in Practice with the Package mlirt
Variance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relati...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2007-02-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v20/i05/paper |
| _version_ | 1818096744208007168 |
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| author | Jean-Paul Fox |
| author_facet | Jean-Paul Fox |
| author_sort | Jean-Paul Fox |
| collection | DOAJ |
| description | Variance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across groups and the group-effects on individuals’ outcomes differ substantially when taking the measurement error in the dependent variable of the model into account. The multilevel model can be extended to handle measurement error using an item response theory (IRT) model, leading to a multilevel IRT model. This extended multilevel model is in particular suitable for the analysis of educational response data where students are nested in schools and schools are nested within cities/countries. |
| first_indexed | 2024-12-10T23:09:29Z |
| format | Article |
| id | doaj.art-a04e44fa0a6a4025af3be4c3de65e7be |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-10T23:09:29Z |
| publishDate | 2007-02-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-a04e44fa0a6a4025af3be4c3de65e7be2022-12-22T01:29:59ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602007-02-01205Multilevel IRT Modeling in Practice with the Package mlirtJean-Paul FoxVariance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across groups and the group-effects on individuals’ outcomes differ substantially when taking the measurement error in the dependent variable of the model into account. The multilevel model can be extended to handle measurement error using an item response theory (IRT) model, leading to a multilevel IRT model. This extended multilevel model is in particular suitable for the analysis of educational response data where students are nested in schools and schools are nested within cities/countries.http://www.jstatsoft.org/v20/i05/paperitem response dataMCMCmultilevel IRT modelFORTRAN |
| spellingShingle | Jean-Paul Fox Multilevel IRT Modeling in Practice with the Package mlirt Journal of Statistical Software item response data MCMC multilevel IRT model FORTRAN |
| title | Multilevel IRT Modeling in Practice with the Package mlirt |
| title_full | Multilevel IRT Modeling in Practice with the Package mlirt |
| title_fullStr | Multilevel IRT Modeling in Practice with the Package mlirt |
| title_full_unstemmed | Multilevel IRT Modeling in Practice with the Package mlirt |
| title_short | Multilevel IRT Modeling in Practice with the Package mlirt |
| title_sort | multilevel irt modeling in practice with the package mlirt |
| topic | item response data MCMC multilevel IRT model FORTRAN |
| url | http://www.jstatsoft.org/v20/i05/paper |
| work_keys_str_mv | AT jeanpaulfox multilevelirtmodelinginpracticewiththepackagemlirt |